Accurate segmentation of fractures in coal rock CT images is important for safe production and the development of coalbed methane.However,to make segment coal rock fractures accurate,the challenges as the following:1)The coal rock CT images have the characteristics which are high background noise, sparse target, weak boundary information, uneven gray level, low contrast etc.; 2)There is no a public dataset of coal rock CT images;3)Limited coal rock CT images samples.In the paper,we proposed adaptive multi-scale feature fusion based residual U-uet(AMSFFRU-uet) for fracture segmentation in coal rock CT images to address the issues.In order to reduce the loss of tiny and weak fractures, dilated residual blocks (DResBlock) are embedded into the U-uet structure, which expand the receptive field and extract fracture information atdifferent scales.Furthermore, for reducing the loss of spatial information during the down-sampling process, feature maps of different sizes in the encoding branch are concatenated by adaptive multi-scale featurefusion module,which is as the input of the first up-sampling in the decoding branch.And we applieda set of comprehensive data augmentation operations to increase the diversity of training samples. Our network,U-net and ResU-net are tested on our dataset of coal rock CT images with 5 different textures.The experimental results show that compared with U-net and ResU-net, our proposed approach improve the average Dice coefficient by 5.1% and 2.9% and the average accuracy by 4.5% and 2%,respectively.Therefore,AMSFFRU-net can achieve better segmentation of coal rock fractures,and has stronger generalization ability and robustness.